1. Field of the Invention
The present invention relates to a method and an apparatus for processing pictures of mobile unit, more particularly, to a method and an apparatus for processing time-series pictures to detect an anomaly such as collision or failure of mobile unit in the pictures.
2. Description of the Related Art
Early detection of a traffic accident can not only enhance a success rate in life saving by speedy rescue operation, but also alleviate accident-related traffic congestion by speedup of the police inspection at the site. Therefore, various kinds of automation in recognition of traffic accident are expected.
In the publication of JP 2001-148019-A whose inventors are the same as those of the present application, there is disclosed a mobile unit anomaly detection method processing time-series pictures to detect an anomaly of mobile unit in pictures, comprising the steps of:
(a) identifying mobile units in a frame picture at a time t on the basis of a correlation between frame pictures at times (t−1) and t;
(b) detecting a feature amount of a relative movement of a second mobile unit relative to a first mobile unit as an observation amount to store observation amounts in time-series as an observation series;
(c) calculating a similarity of the observation series to each reference series to classify a movement between mobile units; and
(d) determining that a collision accident has occurred when the similarity of the observation series to a collision reference series is larger than a predetermined value.
According to this method, it is possible to automatically detect an anomaly such as a collision accident.
However, in a case where a camera angle is low with respect to a road surface, for example, if the second mobile unit approaches the first mobile unit at rest and thereafter, the first mobile unit starts and stops, the second mobile unit overlaps the first mobile unit on pictures at times of the approach and a distance therebetween becomes zero, which is sometimes wrongly determined as a time series pattern of a collision accident
Further, in the above publication, a scalar obtained by quantizing V/(d+ε) is used as an observation amount in the step (b), where V denotes a relative motion vector of the second mobile unit with respect to the first mobile unit and ε denotes a constant to avoid the denominator to be zero.
By using this observation amount, various kinds of movements between mobile units can be classified with a small number of reference series because of the quantization.
However, there has been a problem of impossibility of more detailed classification of movements between mobile units.
In the above publication, in the step (a), by using the identification result of a mobile unit in a frame picture at the time (t−1), the mobile unit in a frame picture at the time t can be identified with ease from the correlation.
However, in a case where mobile units are shot from the front thereof at a low camera angle with respect to a road surface in order to shoot a wide area with one camera to track the mobile units, overlap between mobile units on a picture frequently occurs as shown in FIG. 13. At the time (t−1), mobile units M1 and M2 are identified as one cluster without discriminating the mobile units M1 and M2 from each other. Although a representative motion vector of this cluster is used to identify the cluster including the mobile units M1 and M2 at the time t on the basis of the above-described correlation, accurate identification is disabled since there is a difference in speed between the mobile units M1 and M2. At the next time (t+1), although the mobile unit M2 has been separated from the mobile unit M1, the mobile units M2 and M3 are identified as one cluster since they overlap each other, disabling discrimination of the mobile units M2 and M3 from each other.